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Articles about computational science and data science, neuroscience, and open source solutions. Personal stories are filed under Weekend Stories. Browse all topics here. All posts are CC BY-NC-SA licensed unless otherwise stated. Feel free to share, remix, and adapt the content as long as you give appropriate credit and distribute your contributions under the same license.
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Understanding L1 and L2 regularization in machine learning
Regularization techniques play a vital role in preventing overfitting and enhancing the generalization capability of machine learning models. Among these techniques, L1 and L2 regularization are widely employed for their effectiveness in controlling model complexity. In this blog post, we explore the concepts of L1 and L2 regularization and provide a practical demonstration in Python.
Understanding gradient descent in machine learning
Gradient descent is a fundamental optimization algorithm widely used in machine learning for finding the optimal parameters of a model. It is a powerful technique that enables models to learn from data by iteratively adjusting their parameters to minimize a cost or loss function. In this blog post, we explore the mathematical background of this method and showcase its implementation in Python.
Loading and saving files in Google Colab
Enable I/O support in your notebooks running in Google Colab with just a few additional commands.
Mutual information and its relationship to information entropy
Mutual information is an essential measure in information theory that quantifies the statistical dependence between two random variables. Given its broad applicability, it has become an invaluable tool in diverse fields like machine learning, neuroscience, signal processing, and more. This post explores the mathematical foundations of mutual information and its relationship to information entropy. We will also demonstrate its implementation in some Python examples.
Information entropy
A fundamental concept that plays a pivotal role in quantifying the uncertainty or randomness of a set of data is the information entropy. Information entropy provides a measure of the average amount of information or surprise contained in a random variable. In this blog post, we explore its mathematical foundations and demonstrate its implementation in some Python examples.
Understanding entropy
In physics, entropy is a fundamental concept that plays a crucial role in understanding the behavior of physical systems. It provides a measure of the disorder or randomness within a system, and its study has far-reaching applications across various branches of physics. This blog post aims to provide a brief overview of entropy in order to gain a better understanding of it.
Zen and natural sciences
In this post, I broaden the scope and explore the intersections of Zen and natural sciences more generally.
The Zen of Python
The connection between Zen and programming is not a subjective one at all. For instance, Python has built it directly into its core programming, known as The Zen of Python.
How to get an RSS feed of your Mastodon bookmarks
The third-party service Mastodon Bookmark RSS allows you to subscribe to your Mastodon bookmarks via RSS, so you don’t forget to make use out of them. You can even integrate the feed into your favorite Zettelkasten apps such as DEVONthink and Obsidian.